Legislating Gentle Density From Above? Learning From the Divergent Outcomes of Three Small-Lot Densification Modes in California
Bibliographic record
Abstract
Problem, research strategy, and findings As single-family zoning repeal spreads, the central questions are shifting from whether to do it to whether it is effective and under what circumstances. In this study we compared three recent efforts toward small-lot densification in California: statewide deregulation of accessory dwelling units (ADUs), legislation facilitating lot splitting, and San Diego’s Bonus ADU program. Using a dozen key informant interviews, a survey of municipalities, the economic analysis of prototypical housing developments, and geospatial analysis, we identified the local and contextual factors that have allowed ADU deregulation and bonus ADUs to succeed while lot splitting falls flat. We discuss here the tensions between the ADU ideal, where the homeowner is the change agent, and the missing middle ideal, where it is understood that small developers will be the implementers. ADU reform unlocked latent demand and capability for homeowners to add to their properties; this created a virtuous cycle in which a supportive industry sprang up to assist them. By contrast, lot splitting has failed thus far because it is designed to be implemented by homeowners, who usually lack the ability to take on large, complex projects. San Diego’s Bonus ADU program has had a strong start in part because, despite its name, it is designed to be used by small, professional developers. A limitation of the findings is that follow-up legislation could make lot splitting more successful than it has been, as happened with ADUs; conversely, San Diego recently placed some restrictions on the Bonus ADU program, which may moderate its impressive production figures.Takeaway for practice Policymakers and planners need to be clear about whether there is alignment between the goals of a given small-lot densification policy and the capacity for those intended as the change agents to carry it out.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".